Innovating Works

ACROBAT

Financiado
Hardware Acceleration with Tunable SRAM/IMC Voltages
Deep Neural Networks (DNNs) are the fundamental component in most artificial intelligence applications. With the increasing number of applications based on artificial intelligence, the performance and energy efficiency of architec... Deep Neural Networks (DNNs) are the fundamental component in most artificial intelligence applications. With the increasing number of applications based on artificial intelligence, the performance and energy efficiency of architectures running these algorithms have become crucial, especially for battery-powered platforms. In this work, I propose an energy optimizing memory design framework with a special SRAM/in-memory-computing structure. It also utilizes datapath optimization techniques like quantization and pruning with a fine-level assignment. Compared to other hardware accelerator studies for DNN processing, in this work, I will show that this special memory design, together with the architectural datapath optimization techniques, will have a much better capability of finding the Pareto optimal point in the energy-accuracy trade-off and increase the profitability of the final design. ver más
31/08/2026
BILKENTUNIVERSITY...
239K€
Perfil tecnológico estimado
Duración del proyecto: 49 meses Fecha Inicio: 2022-07-26
Fecha Fin: 2026-08-31

Línea de financiación: concedida

El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-07-26
Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:
HORIZON-MSCA-2021-PF-01-01: MSCA Postdoctoral Fellowships 2021
Cerrada hace 3 años
Presupuesto El presupuesto total del proyecto asciende a 239K€
Líder del proyecto
BILKENT UNIVERSITESI VAKIF No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico TRL 4-5